Project F2 - Discovering Potential Market for the Integration of Public Transportation and Emerging Shared-Mobility Services

US Transportation Collectionhttps://doi.org/10.5281/zenodo.40351092020ZIPDatasetDu, LiliQiu, JiahuaWang, PengFlorida International University. Department of Civil and Environmental EngineeringUniversity of Florida. Dept. of Civil and Coastal EngineeringCitadel, the Military College of South CarolinaSoutheastern Transportation Research, Innovation, Development and Education Center (STRIDE)United States. Department of Transportation. University Transportation Centers (UTC) ProgramUnited States. Department of Transportation. Office of the Assistant Secretary for Research and TechnologySoutheastern Transportation Research, Innovation, Development and Education Center (STRIDE)FloridaUnited StatesChinaAccessData analysisMachine learningPublic transitRidesourcingRidesharingShared mobilitySurveysRidesharing-transit hybrid urban public mobilitySupply-demand marketFirst/last mileTrip dataAccess/egress modeProject F269A355174710This project conducted a comprehensive data analysis to answer two questions: 1) Who are the potential demands with high probability to use intermodal services provided by hybrid systems? (Task 1); 2) Where and when are the supply gaps to coordinate public transit services with shared-mobility service? (Task 2). To accomplish these research objectives, the project team draws from several data sources and approaches to conduct research from both the demand and supply perspectives. Specifically, Task 1 of this study investigated the influential factors that affect transit users\u2019 choices of access and egress modes, including TNC or taxi, drive alone (PNR), carpool (KNR, carpool or shuttle), and micromobility modes (bike-sharing, scooters, etc.), using a transit on-board survey conducted in Spring 2017 for the Orlando metropolitan area. Task 2 of this study used the transit trip data and ridesharing trajectory data in the second ring region of Chengdu, China to develop innovative data analysis and machine learning approaches to explore the transit service gaps in both flexibility and coverage. Together, these efforts provide a snapshot to better understand the potential service gaps and demands (such as first/last mile gaps and demand) for promoting hybrid mobility that integrates shared mobility and public transit. Overall, the outputs of this project will increase the use of sustainable transportation modes, which may reduce urban congestion, emission, and energy consumptions. Thus, the success of this project will help establish an eco-friendly transportation system.121